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PREDICT-HF

Financiado

Predicting Outcome of Rate or Rhythm Control in Patients with Atrial Fibrillatio...

Predicting Outcome of Rate or Rhythm Control in Patients with Atrial Fibrillation and Heart Failure Heart failure (HF) and atrial fibrillation (AF) are common co-morbidities (AF-HF). AF-HF is prevalent in Europe with high rates of hospitalisation and death. AF-HF patients have two treatment options: rate control, where AF is not... ver más

31/08/2026

IMPERIAL COLLEGE O...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND ME... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-05-14

ERC-2019-COG: ERC Consolidator Grant

I+D

Cerrada hace 5 años

0% 100% 100%

3 Participantes

IMPERIAL COLLEGE OF SCIENCE...

1.05M€ | Lider

ACUICOLA MARINA

354.06K€ | Participante

KINGS COLLEGE LONDON

949.15K€ | Participante

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Duración del proyecto: 75 meses Fecha Inicio: 2020-05-14
Fecha Fin: 2026-08-31

Líder del proyecto

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND ME... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Heart failure (HF) and atrial fibrillation (AF) are common co-morbidities (AF-HF). AF-HF is prevalent in Europe with high rates of hospitalisation and death. AF-HF patients have two treatment options: rate control, where AF is not treated but drugs are used to slow the heart rate, or rhythm control, where AF is treated to restore sinus rhythm. Rate control is the first-line treatment, yet specific patient groups do much better under rhythm control. Identifying patients that will do best under rhythm control remains a significant clinical challenge. Potential responders to rhythm control can be identified by their disease history, however, this is often unknown, or their response to treatment, which can only be observed once the therapy has been delivered. We propose to address these challenges by developing patient specific biophysical cardiac models to infer patient history and predict patient response to treatment to inform optimal therapy selection for individual patients. A model for simulating AF-HF in human hearts, representing all four cardiac chambers, will be created. Bayesian uncertainty quantification techniques will be used to combine physical laws, physiology, population data and measurements from individual patients into cardiac models that account for data uncertainty in model parameters and simulation predictions. Patient specific cardiac models will be used to answer three critical clinical questions in prospective studies. Models will be used to predict: if AF led to HF, or HF led to AF in AF-HF patients where the index disease is unknown, response to rhythm control therapy in AF-HF patients and in which AF-HF patient’s rate or rhythm control is best. This proposal outlines an ambitious high risk/return program to address key technical challenges in bringing predictive patient specific models into clinical studies and will apply these innovative techniques to address important clinical questions on the treatment of patients suffering AF-HF.

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